Finite Element Head Model Research Articles

A statistical skull geometry model for children 0-3 years old.

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0–3 YO population. In this study, head CT scans from fifty-six 0–3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.

The effect of tissue anisotropy on the radial and tangential components of the electric field in transcranial direct current stimulation.

Transcranial direct current stimulation (tDCS) is considered to be a promising technique for noninvasive brain stimulation and brain disease therapy. Recent studies have investigated the distribution of the electric field (EF) magnitude over gyri and sulci and the effect of tissue homogeneity with isotropic electrical conductivities. However, it is well known that the skull and white matter (WM) are highly anisotropic electrically, requiring investigations of their anisotropic effects on the magnitude and the directional components of the induced EF due to the high dependency between neuromodulation and the EF direction. In this study, we investigated the effects of the skull and WM anisotropy on the radial and tangential components of the EF via gyri-specific high-resolution finite element head models. For tDCS, three configurations were investigated: the conventional rectangular pad electrode, a 4(cathodes) +1(anode) ring configuration, and a bilateral configuration. The results showed that the skull anisotropy has a crucial influence on the distribution of the radial EF component. The affected cortical regions by the radial EF were reduced about 22% when considering the skull anisotropy in comparison with the regions with the skull isotropy. On the other hand, the WM anisotropy strongly alters the EF directionality, especially within the sulci. The electric current tends to flow radially to the cortical surface with the WM anisotropy. This effect increases the affected cortical areas by the radial EF component within the sulcal regions. Our results suggest that one must examine the distribution of the EF components in tDCS, not just the magnitude of the EF alone.

Prediction of skull fracture risk for children 0-9 months old through validated parametric finite element model and cadaver test reconstruction.

Skull fracture is one of the most common pediatric traumas. However, injury assessment tools for predicting pediatric skull fracture risk is not well established mainly due to the lack of cadaver tests. Weber conducted 50 pediatric cadaver drop tests for forensic research on child abuse in the mid-1980s (Experimental studies of skull fractures in infants, Z Rechtsmed. 92: 87-94, 1984; Biomechanical fragility of the infant skull, Z Rechtsmed. 94: 93-101, 1985). To our knowledge, these studies contained the largest sample size among pediatric cadaver tests in the literature. However, the lack of injury measurements limited their direct application in investigating pediatric skull fracture risks. In this study, 50 pediatric cadaver tests from Weber's studies were reconstructed using a parametric pediatric head finite element (FE) model which were morphed into subjects with ages, head sizes/shapes, and skull thickness values that reported in the tests. The skull fracture risk curves for infants from 0 to 9 months old were developed based on the model-predicted head injury measures through logistic regression analysis. It was found that the model-predicted stress responses in the skull (maximal von Mises stress, maximal shear stress, and maximal first principal stress) were better predictors than global kinematic-based injury measures (peak head acceleration and head injury criterion (HIC)) in predicting pediatric skull fracture. This study demonstrated the feasibility of using age- and size/shape-appropriate head FE models to predict pediatric head injuries. Such models can account for the morphological variations among the subjects, which cannot be considered by a single FE human model.

Influence of stiffness and shape of contact surface on skull fractures and biomechanical metrics of the human head of different population underlateral impacts

The objective of this study was to determine the responses of 5th-percentile female, and 50th- and 95th-percentile male human heads during lateral impacts at different velocities and determine the role of the stiffness and shape of the impacting surface on peak forces and derived skull fracture metrics. A state-of-the-art validated finite element (FE) head model was used to study the influence of different population human heads on skull fracture for lateral impacts. The mass of the FE head model was altered to match the adult size dummies. Numerical simulations of lateral head impacts for 45 cases (15 experiments×3 different population human heads) were performed at velocities ranging from 2.4 to 6.5m/s and three impacting conditions (flat and cylindrical 90D; and flat 40D padding). The entire force-time signals from simulations were compared with experimental mean and upper/lower corridors at each velocity, stiffness (40 and 90 durometer) and shapes (flat and cylindrical) of the impacting surfaces. Average deviation of peak force from the 50th male to 95th male and 5th female were 6.4% and 10.6% considering impacts on the three impactors. These results indicate hierarchy of variables which can be used in injury mitigation efforts.

Influence of head mass on temporo-parietal skull impact using finite element modeling.

The effect of head mass on its biomechanical response during lateral impact to the head is investigated in this computational study. The mass of the head of a state-of-the-art validated finite element head model is altered by ± 10 % from the base value of 4.7 kg. Numerical simulations of lateral head impacts for 30 cases (representing 15 human cadaver experiments × 2 mass configurations) are performed using the LS-DYNA solver at different velocities ranging from 2.4 to 6.5 m/s and three impacting conditions representing different stiffness and shapes of the contact/impact surfaces. Results are compared with the original model using the baseline head mass, thus resulting in a total of 45 simulations. Present findings show that the head mass has greater influence for peak interaction forces and the force has a greater dependency on stiffness of contact surface than the shape. Mass variations have also influence on skull strain energy. Regardless of increase/decrease in skull strain energy influenced by head mass variations used in the computational study, the 50 % fracture tolerance limit was unaltered, which was 544 mJ. The present study gives a better understanding of the mechanism of temporo-parietal skull impact.

The Biomechanical Determinants of Concussion: Finite Element Simulations to Investigate Tissue-Level Predictors of Injury During Sporting Impacts to the Unprotected Head.

Biomechanical studies of concussions have progressed from qualitative observations of head impacts to physical and numerical reconstructions, direct impact measurements, and finite element analyses. Supplementary to a previous study, which investigated maximum principal strain, the current study used a detailed finite element head model to simulate unhelmeted concussion and no-injury head impacts and evaluate the effectiveness of various tissue-level brain injury predictors: strain rate, product of strain and strain rate, cumulative strain damage measure, von Mises stress, and intracranial pressure. Von Mises stress was found to be the most effective predictor of concussion. It was also found that the thalamus and corpus callosum were brain regions with strong associations with concussion. Tentative tolerance limits for tissue-level predictors were proposed in an attempt to broaden the understanding of unhelmeted concussions. For the thalamus, tolerance limits were proposed for a 50% likelihood of concussion: 2.24 kPa, 24.0 s-1, and 2.49 s-1 for von Mises stress, strain rate, and the product of strain and strain rate, respectively. For the corpus callosum, tolerance limits were proposed for a 50% likelihood of concussion: 3.51 kPa, 25.1 s-1, and 2.76 s-1 for von Mises stress, strain rate, and the product of strain and strain rate, respectively.

Investigation of the relationship between facial injuries and traumatic brain injuries using a realistic subject-specific finite element head model

In spite of anatomic proximity of the facial skeleton and cranium, there is lack of information in the literature regarding the relationship between facial and brain injuries. This study aims to correlate brain injuries with facial injuries using finite element method (FEM). Nine common impact scenarios of facial injuries are simulated with their individual stress wave propagation paths in the facial skeleton and the intracranial brain. Fractures of cranio-facial bones and intracranial injuries are evaluated based on the tolerance limits of the biomechanical parameters. General trend of maximum intracranial biomechanical parameters found in nasal bone and zygomaticomaxillary impacts indicates that severity of brain injury is highly associated with the proximity of location of impact to the brain. It is hypothesized that the midface is capable of absorbing considerable energy and protecting the brain from impact. The nasal cartilages dissipate the impact energy in the form of large scale deformation and fracture, with the vomer–ethmoid diverging stress to the “crumpling zone” of air-filled sphenoid and ethmoidal sinuses; in its most natural manner, the face protects the brain. This numerical study hopes to provide surgeons some insight in what possible brain injuries to be expected in various scenarios of facial trauma and to help in better diagnosis of unsuspected brain injury, thereby resulting in decreasing the morbidity and mortality associated with facial trauma.

Combined EEG/MEG Can Outperform Single Modality EEG or MEG Source Reconstruction in Presurgical Epilepsy Diagnosis

We investigated two important means for improving source reconstruction in presurgical epilepsy diagnosis. The first investigation is about the optimal choice of the number of epileptic spikes in averaging to (1) sufficiently reduce the noise bias for an accurate determination of the center of gravity of the epileptic activity and (2) still get an estimation of the extent of the irritative zone. The second study focuses on the differences in single modality EEG (80-electrodes) or MEG (275-gradiometers) and especially on the benefits of combined EEG/MEG (EMEG) source analysis. Both investigations were validated with simultaneous stereo-EEG (sEEG) (167-contacts) and low-density EEG (ldEEG) (21-electrodes). To account for the different sensitivity profiles of EEG and MEG, we constructed a six-compartment finite element head model with anisotropic white matter conductivity, and calibrated the skull conductivity via somatosensory evoked responses. Our results show that, unlike single modality EEG or MEG, combined EMEG uses the complementary information of both modalities and thereby allows accurate source reconstructions also at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. EMEG is furthermore able to reveal the propagation pathway at later time points in agreement with sEEG, while EEG or MEG alone reconstructed only parts of it. Subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations can supply.

Development of a geometrically accurate and adaptable finite element head model for impact simulation: the Naval Research Laboratory–Simpleware Head Model

This study demonstrates a novel model generation methodology that addresses several limitations of conventional finite element head models (FEHM). By operating chiefly in image space, new structures can be incorporated or merged, and the mesh either decimated or refined both locally and globally. This methodology is employed in the development of a highly bio-fidelic FEHM from high-resolution scan data. The model is adaptable and presented here in a form optimised for impact and blast simulations. The accuracy and feasibility of the model are successfully demonstrated against a widely used experimental benchmark in impact loading and through the investigation of potential brain injury under blast overpressure loading.

A QCT-Based Nonsegmentation Finite Element Head Model for Studying Traumatic Brain Injury.

In the existing finite element head models (FEHMs) that are constructed from medical images, head tissues are usually segmented into a number of components according to the interior anatomical structure of the head. Each component is represented by a homogenous material model. There are a number of disadvantages in the segmentation-based finite element head models. Therefore, we developed a nonsegmentation finite element head model with pointwise-heterogeneous material properties and corroborated it by available experiment data. From the obtained results, it was found that although intracranial pressures predicted by the existing (piecewise-homogeneous) and the proposed (pointwise-heterogeneous) FEHM are very similar to each other, strain/stress levels in the head tissues are very different. The maximum peak strains/stresses predicted by the proposed FEHM are much higher than those by the existing FEHM, indicating that piecewise-homogeneous FEHM may have underestimated the stress/strain level induced by impact and thus may be inaccurate in predicting traumatic brain injuries.

Effects of the Variation in Brain Tissue Mechanical Properties on the Intracranial Response of a 6-Year-Old Child.

Brain tissue mechanical properties are of importance to investigate child head injury using finite element (FE) method. However, these properties used in child head FE model normally vary in a large range in published literatures because of the insufficient child cadaver experiments. In this work, a head FE model with detailed anatomical structures is developed from the computed tomography (CT) data of a 6-year-old healthy child head. The effects of brain tissue mechanical properties on traumatic brain response are also analyzed by reconstruction of a head impact on engine hood according to Euro-NCAP testing regulation using FE method. The result showed that the variations of brain tissue mechanical parameters in linear viscoelastic constitutive model had different influences on the intracranial response. Furthermore, the opposite trend was obtained in the predicted shear stress and shear strain of brain tissues caused by the variations of mentioned parameters.

Finite Element Analysis of Soccer Heading

Studies have shown compelling evidence that suggests heading soccer ball might lead to brain trauma. Researchers have attempted to experimentally quantify head acceleration induced by soccer ball heading. A series of linear accelerometers as well as angular accelerometer were used to measure head accelerations. This method however is limited to measuring only head acceleration during the impact. However, it is essential to analyse the acceleration of the brain in addition to the acceleration of the head that takes place during a soccer-heading manoeuvre. Since the motion of the brain is almost impossible to be quantified experimentally, this work focuses on performing finite element (FE) analysis of soccer heading to study the motions of both the head and the brain during the impact. FE model of soccer ball was developed and validated against published experimental data as well as a more detailed model. Moreover, FE model of human head that consists of the skull, facial bones, cerebrospinal fluid (CSF) layer and the brain was also developed and validated against experimental data of blunt impact on human cadaver. Both validated models were assembled to perform the soccer heading simulations. Linear and angular accelerations of the skull and the brain generated are comparable to those of experimental data. However, it has underestimated the angular acceleration due to the absence of neck in the model, but with comparable acceleration profile. Linear and angular accelerations of the brain were found to be almost similar to those of the head, which is contradictory to our initial hypothesis. Further study such as ball impact on instrumented dummy skull is required to corroborate the findings. Nonetheless, the FE models were able to replicate the head accelerations sustained during soccer ball heading satisfactorily. The simulation results show that the models can be employed in finding protective materials that can reduce the accelerations, thus minimising the probability of suffering from long-term brain trauma due to soccer ball heading.

Brain Injury Differences in Frontal Impact Crash Using Different Simulation Strategies.

In the real world crashes, brain injury is one of the leading causes of deaths. Using isolated human head finite element (FE) model to study the brain injury patterns and metrics has been a simplified methodology widely adopted, since it costs significantly lower computation resources than a whole human body model does. However, the degree of precision of this simplification remains questionable. This study compared these two kinds of methods: (1) using a whole human body model carried on the sled model and (2) using an isolated head model with prescribed head motions, to study the brain injury. The distribution of the von Mises stress (VMS), maximum principal strain (MPS), and cumulative strain damage measure (CSDM) was used to compare the two methods. The results showed that the VMS of brain mainly concentrated at the lower cerebrum and occipitotemporal region close to the cerebellum. The isolated head modelling strategy predicted higher levels of MPS and CSDM 5%, while the difference is small in CSDM 10% comparison. It suggests that isolated head model may not equivalently reflect the strain levels below the 10% compared to the whole human body model.

Numerical Simulation of Fluid-Structure Interaction Relations between Skull and Brain Based on ALE and Overlapping Mesh Methods

To simulate skull-CSF-brain interaction relations, a simple finite element head model is established, based on ALE (Arbitrary Lagrangian-Eulerian) and overlapping mesh methods. The responses of head under impact was simulated with this model. The numerical results are coincidence well with the experimental results conducted by Nahum et al. What’s more, it is found that the skull-brain relative displacement and brain injury may be predicted better with the ALE method.

Examination of the protective roles of helmet/faceshield and directionality for human head under blast waves

A parametric study was conducted to delineate the efficacy of personal protective equipment (PPE), such as ballistic faceshields and advanced combat helmets, in the case of a blast. The propagations of blast waves and their interactions with an unprotected head, a helmeted one, and a fully protected finite element head model (FEHM) were modeled. The biomechanical parameters of the brain were recorded when the FEHM was exposed to shockwaves from the front, back, top, and bottom. The directional dependent tissue response of the brain and the variable efficiency of PPE with respect to the blast orientation were two major results of this study.

Computational study of human head response to primary blast waves of five levels from three directions.

Human exposure to blast waves without any fragment impacts can still result in primary blast-induced traumatic brain injury (bTBI). To investigate the mechanical response of human brain to primary blast waves and to identify the injury mechanisms of bTBI, a three-dimensional finite element head model consisting of the scalp, skull, cerebrospinal fluid, nasal cavity, and brain was developed from the imaging data set of a human female. The finite element head model was partially validated and was subjected to the blast waves of five blast intensities from the anterior, right lateral, and posterior directions at a stand-off distance of one meter from the detonation center. Simulation results show that the blast wave directly transmits into the head and causes a pressure wave propagating through the brain tissue. Intracranial pressure (ICP) is predicted to have the highest magnitude from a posterior blast wave in comparison with a blast wave from any of the other two directions with same blast intensity. The brain model predicts higher positive pressure at the site proximal to blast wave than that at the distal site. The intracranial pressure wave invariably travels into the posterior fossa and vertebral column, causing high pressures in these regions. The severities of cerebral contusions at different cerebral locations are estimated using an ICP based injury criterion. Von Mises stress prevails in the cortex with a much higher magnitude than in the internal parenchyma. According to an axonal injury criterion based on von Mises stress, axonal injury is not predicted to be a cause of primary brain injury from blasts.

Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling.

Finite element (FE) models are often used to study the biomechanical effects of traumatic brain injury (TBI). Measures based on mechanical responses, such as principal strain or invariants of the strain tensor, are used as a metric to predict the risk of injury. However, the reliability of inferences drawn from these models depends on the correspondence between the mechanical measures and injury data, as well as the establishment of accurate thresholds of tissue injury. In the current study, a validated anisotropic FE model of the human head is used to evaluate the hypothesis that strain in the direction of fibers (axonal strain) is a better predictor of TBI than maximum principal strain (MPS), anisotropic equivalent strain (AESM) and cumulative strain damage measure (CSDM). An analysis of head kinematics-based metrics, such as head injury criterion (HIC) and brain injury criterion (BrIC), is also provided. Logistic regression analysis is employed to compare binary injury data (concussion/no concussion) with continuous strain/kinematics data. The threshold corresponding to 50% of injury probability is determined for each parameter. The predictive power (area under the ROC curve, AUC) is calculated from receiver operating characteristic (ROC) curve analysis. The measure with the highest AUC is considered to be the best predictor of mTBI. Logistic regression shows a statistical correlation between all the mechanical predictors and injury data for different regions of the brain. Peaks of axonal strain have the highest AUC and determine a strain threshold of 0.07 for corpus callosum and 0.15 for the brainstem, in agreement with previously experimentally derived injury thresholds for reversible axonal injury. For a data set of mild TBI from the national football league, the strain in the axonal direction is found to be a better injury predictor than MPS, AESM, CSDM, BrIC and HIC.

An experimental and numerical investigation of head dynamics due to stick impacts in girls' lacrosse.

A method of investigating head acceleration and intracranial dynamics from stick impacts in girls' and women's lacrosse was developed using headform impact experiments and a finite element head model. Assessing the likelihood of head injury due to stick-head impacts is of interest in girls' and women's lacrosse due to the current lack of head protection during play. Experimental and simulation data were compared to characterize the head acceleration caused by stick-head impacts. Validation against cadaver head impact experiments ensures that the finite element model, with its relatively simple material properties, can provide means to develop a better understanding of the intracranial dynamics during lacrosse stick impacts. Our numerical results showed the peak acceleration at the center of gravity increased linearly with impact force, and was generally in agreement with the experimental data. von Mises stresses and peak principal strains, two common literature injury indicators, were examined within the finite element model, and peak values were below the previously reported thresholds for mild traumatic brain injury. By reconstructing typical in-game, unprotected stick-head impacts, this investigation lays the foundation for a quantitative methodology of injury prediction in girls' and womens' lacrosse.

The Influence of Gyri and Sulci on the Skull-Brain Motion Based on Two Two-Dimensional Finite Element Models

The relative displacement between the skull and brain can be caused by road accidents and leads to permanent health issues or even death. The objective of this study is to investigate the influence of gyri and sulci on skull-brain motion with two two-dimensional finite element head models with detailed structures. The material properties were adopted from the relative literature and assigned to the different model parts. Firstly, frontal impacts were simulated by impacting the head models with the same box-shaped object respectively to enable a comparison between simulation results and experiment data. Next, the same load condition of Nahum’s experiment reported in the literature was used as input to the finite element models. The results indicate that the influence of gyri and sulci on skull-brain motion is not significant.

Combining EEG and MEG for the Reconstruction of Epileptic Activity Using a Calibrated Realistic Volume Conductor Model

To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity. Our results show that even for single interictal spikes, volume conduction effects dominate over noise and need to be taken into account for accurate source analysis. While cerebrospinal fluid and brain anisotropy influence both modalities, only EEG is sensitive to skull conductivity and conductivity calibration significantly reduces the difference in especially depth localization of both modalities, emphasizing its importance for combining EEG and MEG source analysis. On the other hand, localization differences which are due to the distinct sensitivity profiles of EEG and MEG persist. In case of a moderate error in skull conductivity, combined source analysis results can still profit from the different sensitivity profiles of EEG and MEG to accurately determine location, orientation and strength of the underlying sources. On the other side, significant errors in skull modeling are reflected in EEG reconstruction errors and could reduce the goodness of fit to combined datasets. For combined EEG and MEG source analysis, we therefore recommend calibrating skull conductivity using additionally acquired SEP/SEF data.